Application and comparison of different grid-based hydrological models in the Laoha River basin

2009 ◽  
Author(s):  
Xingze Wang ◽  
Xiumin Song ◽  
Yunhong Xue ◽  
Peng Li ◽  
Linlong Bai ◽  
...  
Keyword(s):  
River Basin ◽  
Hydrological Models ◽  
Grid Based

scholarly journals Aplicação do Tank Model como Ferramenta de Gestão na Bacia do Rio Perdizes – Cambará do Sul/RS

2021 ◽  
Vol 14 (2) ◽  
pp. 1143
Author(s):  
Karla Campagnolo ◽  
Sofia Melo Vasconcellos ◽  
Vinicius Santanna Castiglio ◽  
Marina Refatti Fagundes ◽  
Masato Kobiyama
Keyword(s):  
River Basin ◽  
National Park ◽  
Management Tool ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Moisture Index ◽  
Tank Model ◽  
Calibration And Validation ◽  
Historical Series ◽  
Soil Moisture Index

A representação do processo precipitação-vazão por meio de modelos hidrológicos conceituais visa quantificar o volume escoado em uma bacia como consequência de uma determinada precipitação. Aliados a eles, os índices têm sido uma ferramenta útil para quantificar eventos extremos, como o Soil Moisture Index (TMI) que foi formulado a partir do modelo hidrológico Tank Model. Desta forma, o objetivo deste trabalho foi aplicar o Tank Model para a bacia do rio Perdizes, em Cambará do Sul (RS), e avaliar o desempenho do TMI para prever a ocorrência de cheias, limiar este utilizado para o fechamento da Trilha do rio do Boi, no Parque Nacional de Aparados da Serra (PNAS). Os dados utilizados na simulação foram obtidos pelas estações meteorológica e fluviométrica instaladas na bacia. Após a calibração e validação de três séries históricas no Tank Model, os valores obtidos do TMI foram comparados com os dias que a Trilha foi fechada, a partir de altos níveis registrados no rio Perdizes. O TMI demonstrou que o nível utilizado para fechar a Trilha do rio do Boi correspondeu a cheias em 72% das vezes. Portanto, o TMI mostrou bom desempenho ao indicar a ocorrência de cheias na área estudada, sendo uma ferramenta útil para a tomada de decisões na gestão do PNAS.  Application of the Tank Model as a Management Tool in the Perdizes River Basin - Cambará do Sul/RS.ABSTRACTThe representation of the rainfall-runoff process by means of conceptual hydrological models aims to quantify the volume drained in a basin as result of a specific precipitation. Allied to them, the indices have been a useful tool to quantify extreme events, such as the Tank Moisture Index (TMI) which was formulated from the Tank Model. Thus, the objective of this work was to apply the Tank Model to the Perdizes river basin, in Cambará do Sul (RS), and to evaluate the performance of the TMI to predict the occurrence of floods, the threshold used for the closure of the Rio do Boi trail, in the Aparados da Serra National Park (PNAS). The data used in the simulation were obtained at the meteorological and fluviometric stations installed in the basin. After the calibration and validation of three historical series in the Tank Model, the values obtained in the TMI were compared with the days when the Trail was closed, from high levels recorded in the Perdizes river. The average TMI values demonstrated that the level used to close the Rio do Boi Trail corresponded to floods 72% of the time, and the median, 75%. Therefore, the TMI showed good performance in indicating the occurrence of floods in the study area, being a useful tool for decision making in the PNAS management.Keywords: Tank Moisture Index, trail closure, Aparados da Serra National Park.

scholarly journals The bias in GRACE estimates of continental water storage variations

2006 ◽  
Vol 3 (6) ◽  
pp. 3557-3594 ◽  
Author(s):  
R. Klees ◽  
E. A. Zapreeva ◽  
H. C. Winsemius ◽  
H. H. G. Savenije
Keyword(s):  
River Basin ◽  
Southern Africa ◽  
A Priori ◽  
Water Storage ◽  
Target Area ◽  
Hydrological Models ◽  
A Priori Information ◽  
Basin Scale ◽  
Priori Information ◽  
Gravity Field Models

Abstract. The estimation of terrestrial water storage variations at river basin scale is among the best documented applications of the GRACE (Gravity and Climate Experiment) satellite gravity mission. In particular, it is expected that GRACE closes the water balance at river basin scale and allows the verification, improvement and modeling of the related hydrological processes by combining GRACE amplitude estimates with hydrological models' output and in-situ data. When computing monthly mean storage variations from GRACE gravity field models, spatial filtering is mandatory to reduce GRACE errors, but at the same time yields biased amplitude estimates. The objective of this paper is three-fold. Firstly, we want to compute and analyze amplitude and time behaviour of the bias in GRACE estimates of monthly mean water storage variations for several target areas in Southern Africa. In particular, we want to know the relation between bias and the choice of the filter correlation length, the size of the target area, and the amplitude of mass variations inside and outside the target area. Secondly, we want to know to what extent the bias can be corrected for using a priori information about mass variations. Thirdly, we want to quantify errors in the estimated bias due to uncertainties in the a priori information about mass variations that are used to compute the bias. The target areas are located in Southern Africa around the Zambezi river basin. The latest release of monthly GRACE gravity field models have been used for the period from January 2003 until March 2006. An accurate and properly calibrated regional hydrological model has been developed for this area and its surroundings and provides the necessary a priori information about mass variations inside and outside the target areas. The main conclusion of the study is that spatial smoothing significantly biases GRACE estimates of the amplitude of annual and monthly mean water storage variations. For most of the practical applications, the bias will be positive, which implies that GRACE underestimates the amplitudes. The bias is mainly determined by the filter correlation length; in the case of 1000 km smoothing, which is shown to be an appropriate choice for the target areas, the annual bias attains values up to 50% of the annual storage; the monthly bias is even larger with a maximum value of 75% of the monthly storage. A priori information about mass variations can provide reasonably accurate estimates of the bias, which significantly improves the quality of GRACE water storage amplitudes. For the target areas in Southern Africa, we show that after bias correction, GRACE annual amplitudes differ between 0 and 30 mm from the output of a regional hydrological model, which is between 0% and 25% of the storage. Annual phase shifts are small, not exceeding 0.25 months, i.e. 7.5 deg. Our analysis suggests that bias correction of GRACE water storage amplitudes is indispensable if GRACE is used to calibrate hydrological models.

scholarly journals Data assimilation of GRACE terrestrial water storage estimates into a regional hydrological model of the Rhine River basin

2015 ◽  
Vol 19 (4) ◽  
pp. 2079-2100 ◽  
Author(s):  
N. Tangdamrongsub ◽  
S. C. Steele-Dunne ◽  
B. C. Gunter ◽  
P. G. Ditmar ◽  
A. H. Weerts
Keyword(s):  
Correlation Coefficient ◽  
River Basin ◽  
Hydrological Cycle ◽  
Water Storage ◽  
Model Parameters ◽  
Hydrological Models ◽  
Rhine River ◽  
Terrestrial Water Storage ◽  
Grace Data ◽  
Terrestrial Water

Abstract. The ability to estimate terrestrial water storage (TWS) realistically is essential for understanding past hydrological events and predicting future changes in the hydrological cycle. Inadequacies in model physics, uncertainty in model land parameters, and uncertainties in meteorological data commonly limit the accuracy of hydrological models in simulating TWS. In an effort to improve model performance, this study investigated the benefits of assimilating TWS estimates derived from the Gravity Recovery and Climate Experiment (GRACE) data into the OpenStreams wflow_hbv model using an ensemble Kalman filter (EnKF) approach. The study area chosen was the Rhine River basin, which has both well-calibrated model parameters and high-quality forcing data that were used for experimentation and comparison. Four different case studies were examined which were designed to evaluate different levels of forcing data quality and resolution including those typical of other less well-monitored river basins. The results were validated using in situ groundwater (GW) and stream gauge data. The analysis showed a noticeable improvement in GW estimates when GRACE data were assimilated, with a best-case improvement of correlation coefficient from 0.31 to 0.53 and root mean square error (RMSE) from 8.4 to 5.4 cm compared to the reference (ensemble open-loop) case. For the data-sparse case, the best-case GW estimates increased the correlation coefficient from 0.46 to 0.61 and decreased the RMSE by 35%. For the average improvement of GW estimates (for all four cases), the correlation coefficient increases from 0.6 to 0.7 and the RMSE was reduced by 15%. Only a slight overall improvement was observed in streamflow estimates when GRACE data were assimilated. Further analysis suggested that this is likely due to sporadic short-term, but sizeable, errors in the forcing data and the lack of sufficient constraints on the soil moisture component. Overall, the results highlight the benefit of assimilating GRACE data into hydrological models, particularly in data-sparse regions, while also providing insight on future refinements of the methodology.

scholarly journals Model Uncertainty Analysis Methods for Semi-Arid Watersheds with Different Characteristics: A Comparative SWAT Case Study

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1177 ◽  
Author(s):  
Lufang Zhang ◽  
Baolin Xue ◽  
Yuhui Yan ◽  
Guoqiang Wang ◽  
Wenchao Sun ◽  
...  
Keyword(s):  
Uncertainty Analysis ◽  
River Basin ◽  
Model Uncertainty ◽  
Assessment Tool ◽  
Rapid Development ◽  
Swat Model ◽  
Observation Data ◽  
Hydrological Models ◽  
Distributed Hydrological Models ◽  
Different Characteristics

Distributed hydrological models play a vital role in water resources management. With the rapid development of distributed hydrological models, research into model uncertainty has become a very important field. When studying traditional hydrological model uncertainty, it is very common to use multisite observation data to evaluate the performance of the model in the same watershed, but there are few studies on uncertainty in watersheds with different characteristics. This study is based on the Soil and Water Assessment Tool (SWAT) model, and uses two common methods: Sequential Uncertainty Fitting Version 2 (SUFI-2) and Generalized Likelihood Uncertainty Estimation (GLUE) for uncertainty analysis. We compared these methods in terms of parameter uncertainty, model prediction uncertainty, and simulation effects. The Xiaoqing River basin and the Xinxue River basin, which have different characteristics, including watershed geography and scale, were used for the study areas. The results show that the GLUE method had better applicability in the Xiaoqing River basin, and that the SUFI-2 method provided more reasonable and accurate analysis results in the Xinxue River basin; thus, the applicability was higher. The uncertainty analysis method is affected to some extent by the characteristics of the watershed.

scholarly journals Analysis of aggregation and disaggregation effects for grid-based hydrological models and the development of improved precipitation disaggregation procedures for GCMs

1999 ◽  
Vol 3 (1) ◽  
pp. 95-108 ◽  
Author(s):  
H. S. Wheater ◽  
T. J. Jolley ◽  
C. Onof ◽  
N. Mackay ◽  
R. E. Chandler
Keyword(s):  
Soil Moisture ◽  
Time Scale ◽  
Scale Dependence ◽  
Moisture Distribution ◽  
Image Processing Technique ◽  
Simple Extension ◽  
Hydrological Models ◽  
Radar Rainfall ◽  
Spatial Coverage ◽  
Grid Based

Abstract. Appropriate representation of hydrological processes within atmospheric General Circulation Models (GCMs) is important with respect to internal model dynamics (e.g. surface feedback effects on atmospheric fluxes, continental runoff production) and to simulation of terrestrial impacts of climate change. However, at the scale of a GCM grid-square, several methodological problems arise. Spatial disaggregation of grid-square average climatological parameters is required in particular to produce appropriate point intensities from average precipitation. Conversely, aggregation of land surface heterogeneity is necessary for grid-scale or catchment scale application. The performance of grid-based hydrological models is evaluated for two large (104km2) UK catchments. Simple schemes, using sub-grid average of individual land use at 40 km scale and with no calibration, perform well at the annual time-scale and, with the addition of a (calibrated) routing component, at the daily and monthly time-scale. Decoupling of hillslope and channel routing does not necessarily improve performance or identifiability. Scale dependence is investigated through application of distribution functions for rainfall and soil moisture at 100 km scale. The results depend on climate, but show interdependence of the representation of sub-grid rainfall and soil moisture distribution. Rainfall distribution is analysed directly using radar rainfall data from the UK and the Arkansas Red River, USA. Among other properties, the scale dependence of spatial coverage upon radar pixel resolution and GCM grid-scale, as well as the serial correlation of coverages are investigated. This leads to a revised methodology for GCM application, as a simple extension of current procedures. A new location-based approach using an image processing technique is then presented, to allow for the preservation of the spatial memory of the process.

New Multisite Cascading Calibration Approach for Hydrological Models: Case Study in the Red River Basin Using the VIC Model

2016 ◽  
Vol 21 (2) ◽  
pp. 05015019 ◽  
Author(s):  
Xianwu Xue ◽  
Ke Zhang ◽  
Yang Hong ◽  
Jonathan J. Gourley ◽  
Wayne Kellogg ◽  
...  
Keyword(s):  
River Basin ◽  
Red River ◽  
Hydrological Models ◽  
Vic Model ◽  
Red River Basin ◽  
Calibration Approach

scholarly journals Correcting Errors in Streamflow Forecast Ensemble Mean and Spread

2008 ◽  
Vol 9 (1) ◽  
pp. 132-148 ◽  
Author(s):  
Andrew W. Wood ◽  
John C. Schaake
Keyword(s):  
River Basin ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
Summer Period ◽  
Streamflow Forecast ◽  
Ensemble Mean ◽  
Ensemble Streamflow Prediction ◽  
Deterministic Components ◽  
Seasonal Streamflow

Abstract When hydrological models are used for probabilistic streamflow forecasting in the Ensemble Streamflow Prediction (ESP) framework, the deterministic components of the approach can lead to errors in the estimation of forecast uncertainty, as represented by the spread of the forecast ensemble. One avenue for correcting the resulting forecast reliability errors is to calibrate the streamflow forecast ensemble to match observed error characteristics. This paper outlines and evaluates a method for forecast calibration as applied to seasonal streamflow prediction. The approach uses the correlation of forecast ensemble means with observations to generate a conditional forecast mean and spread that lie between the climatological mean and spread (when the forecast has no skill) and the raw forecast mean with zero spread (when the forecast is perfect). Retrospective forecasts of summer period runoff in the Feather River basin, California, are used to demonstrate that the approach improves upon the performance of traditional ESP forecasts by reducing errors in forecast mean and improving spread estimates, thereby increasing forecast reliability and skill.

scholarly journals Climate change impact on river flow extremes in the Upper Blue Nile River basin

2018 ◽  
Vol 10 (4) ◽  
pp. 759-781 ◽  
Author(s):  
Hadush K. Meresa ◽  
Mulusew T. Gatachew
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Change Impact ◽  
River Flow ◽  
Nile River ◽  
Hydrological Models ◽  
Hydrological Extremes ◽  
Dry Spell ◽  
Change Impact ◽  
Blue Nile River Basin

Abstract This paper aims to study climate change impact on the hydrological extremes and projected precipitation extremes in far future (2071–2100) period in the Upper Blue Nile River basin (UBNRB). The changes in precipitation extremes were derived from the most recent AFROCORDEX climate data base projection scenarios compared to the reference period (1971–2000). The climate change impacts on the hydrological extremes were evaluated using three conceptual hydrological models: GR4 J, HBV, and HMETS; and two objective functions: NSE and LogNSE. These hydrological models are calibrated and validated in the periods 1971–2000 and 2001–2010, respectively. The results indicate that the wet/dry spell will significantly decrease/increase due to climate change in some sites of the region, while in others, there is increase/decrease in wet/dry spell but not significantly, respectively. The extreme river flow will be less attenuated and more variable in terms of magnitude, and more irregular in terms of seasonal occurrence than at present. Low flows are projected to increase most prominently for lowland sites, due to the combined effects of projected decreases in Belg and Bega precipitation, and projected increases in evapotranspiration that will reduce residual soil moisture in Bega and Belg seasons.

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